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Abstract.Chlorophyll content is an important parameter to evaluate the growth situation of crops. In order to detect the chlorophyll content of potato leaves, the hyperspectral images of potato leaves were analyzed and red edge parameter was used to inverse the chlorophyll parameter. According to the chlorophyll detection model, the visualization map of chlorophyll was drawn. First, hyperspectral images of 35 potato leaves were collected and divided into 220 regions of interesting (ROI). Meanwhile, the SPAD values of these sampling ROI were collected by a hand-held SPAD detector to indicate the chlorophyll content respectively. Second, the average spectral reflectance of selected ROI was extracted by using ENVI software and used for the chlorophyll content detection. However, the red-edge wavelength, strong correlation with crop parameters (chlorophyll, total nitrogen content, etc.), was analyzed to reduce the data dimension. Thus, the "red-edge" wavelength was calculated by the linear four-point interpolation method. Third, the models were established to detect the chlorophyll content of potato leaves. The result showed that the "red-edge" wavelength calculated by 4 wavelengths at of 670, 700, 740 and 780 nm could be used to detect the chlorophyll content. The exponential model was built, which R²_v reached 0.6180, and R²_v reached 0.7620.The linear four-point interpolation method was used to inverse the chlorophyll content of each pixel in potato leaves. The visualization of chlorophyll content was mapped by pseudo-color image processing to provide support for the non-destructive detection of chlorophyll in large-area potato crops.